Predicting links in information networks requires deep understanding and careful modeling
of network structure. Network embedding, which aims to learn low-dimensional …

Complex networks are widely used to represent an abundance of real-world relations
ranging from social networks to brain networks. Inferring missing links or predicting future …

To enjoy more social network services, users nowadays are usually involved in multiple
online sites at the same time. Aligned social networks provide more information to alleviate …

Network embedding, which aims to learn low-dimensional representations of nodes, has
been used for various graph related tasks including visualization, link prediction, and node …

In real-world complex networks, understanding the dynamics of their evolution has been of
great interest to the scientific community. Predicting non-existent but probable links is an …

Link prediction is a fundamental task in network analysis, with the objective of predicting
missing or potential links. While existing studies have mainly concentrated on single …

Link prediction is an important task in social network analysis and mining because of its
various applications. A large number of link prediction methods have been proposed …

Network embedding aims at learning a low-dimensional dense vector for each node in the
network. In recent years, it has attracted great research attention due to its wide applications …

A network is a form of data representation and is widely used in many fields. For example, in
social networks, we regard nodes as individuals or groups, and the edges between nodes …

In this paper, we consider new formulation of graph embedding algorithm, while learning
node and edge representation under common constraints. We evaluate our approach on …